Improving road trip attraction recommendations by resolving conflicting preferences: a knowledge-enhanced non-compensatory group decision approach

Purpose In recent years, road trips have become a popular travel mode in China. However, existing attraction recommendation methods often overlook conflicting preferences among tourists and key factors specific to road trips, resulting in suboptimal recommendations. To address this issue, we propose a knowledge-enhanced non-compensatory group decision-making approach to improve the accuracy of road trip attraction recommendations. Design/methodology/approach First, our approach constructs a comprehensive tourism knowledge graph by integrating information about both tourists and attractions. Second, an 11-dimensional modeling framework is proposed to better portray tourist preferences and attraction characteristics in the road trip context. Finally, the non-compensatory group decision-making algorithm, Elimination Et Choix Traduisant la REalité III (ELECTRE-III), is applied to model each tourist's preferences within the group and rank attractions for the tourist group. Findings Experimental results demonstrate the effectiveness of the proposed method in road trip attraction recommendation. Compared to existing approaches, our method maintains a recommendation failure rate below 15% across varying levels of conflict rates and group sizes, consistently outperforming all baseline methods. Statistical analysis further confirms that the non-compensatory mechanism effectively identifies attractions that align with the collective preferences of tourist groups. Originality/value This paper proposes a novel method for recommending attractions to road trip tourist groups by integrating non-compensatory group decision-making with knowledge integration. The approach effectively incorporates individual preferences while maintaining a low recommendation failure rate, thereby enhancing both recommendation performance and overall tourist satisfaction.